{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Text Analysis with SpaCy and Scikit-Learn\n",
    "\n",
    "This notebook was originally prepared for the workshop [Advanced Text Analysis with SpaCy and Scikit-Learn](http://dhweek.nycdh.org/event/advanced-text-analysis-with-spacy-and-scikit-learn/), presented as part of NYCDH Week 2017. Here, we try out features of the SpaCy library for natural language processing. We also do some statistical analysis using from the scikit-learn library. \n",
    "\n",
    "Prepared by [Jonathan Reeve](http://jonreeve.com) ([Group for Experimental Methods in the Humanities](http://xpmethod.plaintext.in/), Columbia University). All code here is licensed under the MIT License. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Installing this software is easiest on a Linux-like system. If you're not already running Linux, you can easily download a distribution and copy it to a USB disk, which you can then boot from. I recommend getting [DH-USB](https://github.com/DH-Box/dh-usb), a Linux-based operating system made for the Digital Humanities. DH-USB already has most of this software installed, including the SpaCy data, but might need `ete3`. \n",
    "\n",
    "If you have a different Linux-like system, (including, to greater or lesser degrees, Ubuntu, MacOS, Cygwin, and Bash for Windows), you should be able to run these commands to install SpaCy, Scikit-Learn, Pandas, and the other required libraries. Ete3 is a library for tree visualization which is optional. \n",
    "\n",
    "```bash\n",
    "sudo pip install spacy scikit-learn pandas ete3\n",
    "```\n",
    "\n",
    "Note that if your system has Python 2 as the default, instead of Python 3, you might have to run `pip3` instead of `pip`. \n",
    "\n",
    "Now download the SpaCy data with this command: \n",
    "\n",
    "```bash\n",
    "python -m spacy.en.download all\n",
    "```\n",
    "\n",
    "To get my sent2tree library and all the sample data, simply `git clone` the repository where this notebook lives: \n",
    "\n",
    "```bash\n",
    "git clone https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017.git\n",
    "```\n",
    "\n",
    "## Installation on DH Box\n",
    "\n",
    "You can also use the cloud-based Digital Humanities workstation platform [DH Box](http://dhbox.org) to run the code in this notebook. The installation procedure is slightly different. First, run these commands in the Command Line tab: \n",
    "\n",
    "```bash\n",
    "sudo pip3 install spacy scikit-learn seaborn\n",
    "sudo pip3 install --pre ete3 \n",
    "sudo apt-get update\n",
    "sudo apt-get install python3-pyqt4 # Required by ete3\n",
    "sudo python3 -m spacy.en.download all\n",
    "git clone https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017.git\n",
    "```\n",
    "\n",
    "Then open the Jupyter Notebook tab, log in, navigate to the directory `advanced-text-analysis-workshop-2017`, and open this notebook, `advanced-text-analysis.ipynb`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Display plots in this notebook, instead of externally. \n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "%matplotlib inline\n",
    "\n",
    "# The following are optional dependencies. \n",
    "# Feel free to comment these out. \n",
    "# Sent2tree uses the sent2tree.py module in this repository. \n",
    "from sent2tree import sentenceTree\n",
    "import ete3 \n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This command might take a little while. \n",
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample data is the script of the 1975 film _Monty Python and the Holy Grail_, taken from the NLTK Book corpus, and the Project Gutenberg edition of Jane Austen's novel _Pride and Prejudice_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grail_raw = open('grail.txt').read()\n",
    "pride_raw = open('pride.txt').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parse the texts. These commands might take a little while. \n",
    "grail = nlp(grail_raw)\n",
    "pride = nlp(pride_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Document\n",
    "\n",
    "Each SpaCy document is already tokenized into words, which are accessible by iterating over the document: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "﻿It"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pride[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "﻿It is a truth universally acknowledged, that a single"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pride[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also iterate over the sentences. `doc.sents` is a generator object, so we can use `next()`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "﻿It is a truth universally acknowledged, that a single man in possession\n",
       "of a good fortune, must be in want of a wife.\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(pride.sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can force it into a list, and then do things with it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Darcy, as well as Elizabeth, really loved them; and they were both ever\n",
       "sensible of the warmest gratitude towards the persons who, by bringing\n",
       "her into Derbyshire, had been the means of uniting them."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prideSents = list(pride.sents)\n",
    "prideSents[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Christ!"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(grail.sents)[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, let's find the longest sentence(s) in _Pride and Prejudice_: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Every lingering struggle in his favour grew fainter\n",
       " and fainter; and in farther justification of Mr. Darcy, she could not\n",
       " but allow that Mr. Bingley, when questioned by Jane, had long ago\n",
       " asserted his blamelessness in the affair; that proud and repulsive as\n",
       " were his manners, she had never, in the whole course of their\n",
       " acquaintance--an acquaintance which had latterly brought them much\n",
       " together, and given her a sort of intimacy with his ways--seen anything\n",
       " that betrayed him to be unprincipled or unjust--anything that spoke him\n",
       " of irreligious or immoral habits; that among his own connections he was\n",
       " esteemed and valued--that even Wickham had allowed him merit as a\n",
       " brother, and that she had often heard him speak so affectionately of his\n",
       " sister as to prove him capable of _some_ amiable feeling; that had his\n",
       " actions been what Mr. Wickham represented them, so gross a violation of\n",
       " everything right could hardly have been concealed from the world; and\n",
       " that friendship between a person capable of it, and such an amiable man\n",
       " as Mr. Bingley, was incomprehensible.\n",
       " ]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prideSentenceLengths = [len(sent) for sent in prideSents]\n",
    "[sent for sent in prideSents if len(sent) == max(prideSentenceLengths)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Words\n",
    "\n",
    "Each word has a crazy number of properties: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "universally"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pride[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ancestors',\n",
       " 'check_flag',\n",
       " 'children',\n",
       " 'cluster',\n",
       " 'conjuncts',\n",
       " 'dep',\n",
       " 'dep_',\n",
       " 'doc',\n",
       " 'ent_id',\n",
       " 'ent_id_',\n",
       " 'ent_iob',\n",
       " 'ent_iob_',\n",
       " 'ent_type',\n",
       " 'ent_type_',\n",
       " 'has_repvec',\n",
       " 'has_vector',\n",
       " 'head',\n",
       " 'i',\n",
       " 'idx',\n",
       " 'is_alpha',\n",
       " 'is_ancestor',\n",
       " 'is_ancestor_of',\n",
       " 'is_ascii',\n",
       " 'is_bracket',\n",
       " 'is_digit',\n",
       " 'is_left_punct',\n",
       " 'is_lower',\n",
       " 'is_oov',\n",
       " 'is_punct',\n",
       " 'is_quote',\n",
       " 'is_right_punct',\n",
       " 'is_space',\n",
       " 'is_stop',\n",
       " 'is_title',\n",
       " 'lang',\n",
       " 'lang_',\n",
       " 'left_edge',\n",
       " 'lefts',\n",
       " 'lemma',\n",
       " 'lemma_',\n",
       " 'lex_id',\n",
       " 'like_email',\n",
       " 'like_num',\n",
       " 'like_url',\n",
       " 'lower',\n",
       " 'lower_',\n",
       " 'n_lefts',\n",
       " 'n_rights',\n",
       " 'nbor',\n",
       " 'norm',\n",
       " 'norm_',\n",
       " 'orth',\n",
       " 'orth_',\n",
       " 'pos',\n",
       " 'pos_',\n",
       " 'prefix',\n",
       " 'prefix_',\n",
       " 'prob',\n",
       " 'rank',\n",
       " 'repvec',\n",
       " 'right_edge',\n",
       " 'rights',\n",
       " 'sentiment',\n",
       " 'shape',\n",
       " 'shape_',\n",
       " 'similarity',\n",
       " 'string',\n",
       " 'subtree',\n",
       " 'suffix',\n",
       " 'suffix_',\n",
       " 'tag',\n",
       " 'tag_',\n",
       " 'text',\n",
       " 'text_with_ws',\n",
       " 'vector',\n",
       " 'vector_norm',\n",
       " 'vocab',\n",
       " 'whitespace_']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[prop for prop in dir(pride[4]) if not prop.startswith('_')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using just the indices (`.i`), we can make a lexical dispersion plot for the occurrences of that word in the novel. (This is just the SpaCy equivalent of the lexical dispersion plot from the NLTK Book, chapter 1.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pride[4].i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def locations(needle, haystack): \n",
    "    \"\"\" \n",
    "    Make a list of locations, bin those into a histogram, \n",
    "    and finally put it into a Pandas Series object so that we\n",
    "    can later make it into a DataFrame. \n",
    "    \"\"\"\n",
    "    return pd.Series(np.histogram(\n",
    "        [word.i for word in haystack \n",
    "         if word.text.lower() == needle], bins=50)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f01e0232198>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f01df5c36d8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f01df528c18>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f01df4faf28>], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zw0f932Tryjh+UdbK/72XTbifK07a0dnRt5XxOq/FxCbzWthCfauO1z6+gKeb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\nfXwBrUbNd+9MlezAQUiK8iU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B0nR5AsstbWHbwWJqLu0+rZ8fxYZR3n0azLwurGpn24Fi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\nxzB0EhERERERkccwdBIREREREZHH/D9rMTSFGr7+WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e450e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I have no idea why I have to keep running this. \n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "\n",
    "pd.DataFrame(\n",
    "    {name: locations(name.lower(), pride) \n",
    "     for name in ['Elizabeth', 'Darcy', 'Jane', 'Bennet']}\n",
    ").plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See if you can tell which characters end up getting together at the end, just based on this plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Named Entities\n",
    "\n",
    "Named entities can be accessed through `doc.ents`. Let's find all the types of named entities from _Monty Python and the Holy Grail_: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CARDINAL',\n",
       " 'DATE',\n",
       " 'EVENT',\n",
       " 'GPE',\n",
       " 'LANGUAGE',\n",
       " 'MONEY',\n",
       " 'NORP',\n",
       " 'ORDINAL',\n",
       " 'ORG',\n",
       " 'PERSON',\n",
       " 'PRODUCT',\n",
       " 'QUANTITY',\n",
       " 'TIME',\n",
       " 'WORK_OF_ART'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([w.label_ for w in grail.ents])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about those that are works of art? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[the Holy Pin, the Holy Grail in the Castle of uuggggggh', the Holy Grail]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ent for ent in grail.ents if ent.label_ == 'WORK_OF_ART']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Place names? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[England,\n",
       " Camelot,\n",
       " Mercea,\n",
       " Divine,\n",
       " Camelot,\n",
       " Yeaaah,\n",
       " Yeaah,\n",
       " Camelot,\n",
       " Lancelot,\n",
       " Camelot,\n",
       " Camelot,\n",
       " England,\n",
       " Camelot,\n",
       " Castle Anthrax,\n",
       " Britain,\n",
       " Camelot,\n",
       " Nador,\n",
       " Spring,\n",
       " Spring,\n",
       " Antioch,\n",
       " France,\n",
       " Cornwall,\n",
       " Assyria]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ent for ent in grail.ents if ent.label_ == 'GPE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Organizations? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ARTHUR',\n",
       " 'Aramaic',\n",
       " 'Armaments',\n",
       " 'BEDEVERE',\n",
       " 'Caerbannog',\n",
       " 'Camelot',\n",
       " 'Chicken of Bristol',\n",
       " 'Excalibur',\n",
       " 'HISTORIAN',\n",
       " 'Holy Grail',\n",
       " 'I.',\n",
       " 'KNIGHT',\n",
       " 'Knights',\n",
       " 'Knights of Ni',\n",
       " 'Knights of the Round Table',\n",
       " 'Launcelot',\n",
       " 'MAN',\n",
       " 'Olfin Bedwere of',\n",
       " 'Pie Iesu',\n",
       " 'Robinson',\n",
       " 'The Black Knight',\n",
       " 'The Bridge of Death',\n",
       " 'The Castle Anthrax',\n",
       " 'The Holy Hand Grenade of',\n",
       " 'The Lady of',\n",
       " 'Uther Pendragon',\n",
       " \"the BLACK KNIGHT's\",\n",
       " 'the Bridge of Death',\n",
       " 'the Castle Anthrax',\n",
       " 'the Court of Camelot',\n",
       " 'the Holy Grail',\n",
       " 'the Round Table',\n",
       " 'the Tall Tower'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(list([ent.string.strip() for ent in grail.ents if ent.label_ == 'ORG']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about groups of people? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'African ',\n",
       " 'Britons',\n",
       " 'English ',\n",
       " 'European ',\n",
       " 'French',\n",
       " 'French ',\n",
       " 'Psalms',\n",
       " 'Summer',\n",
       " 'Summer '}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([ent.string for ent in grail.ents if ent.label_ == 'NORP'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"French\" here refers to French _people_, not the French language. We can verify that by getting all the sentences in which this particular type of entity occurs: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[FRENCH GUARD: I'm French!  ,\n",
       " Well, now, uh, Launcelot, Galahad, and I, uh, wait until nightfall, and then leap out of the rabbit, taking the French, uh, by surprise.  ,\n",
       " The ferocity of the French taunting took him completely by surprise, and Arthur became convinced that a new strategy was required if the quest for the Holy Grail were to be brought to a successful conclusion.  ,\n",
       " So, we French fellows outwit you a second time!,\n",
       " So, you think you could out-clever us French folk with your silly knees-bent running about advancing behaviour?!  ,\n",
       " ARTHUR: Stand by for attack! [exciting music] [music stops] [silence] French persons!]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frenchPeople = [ent for ent in grail.ents if ent.label_ == 'NORP' and ent.string.strip() == 'French']\n",
    "[ent.sent for ent in frenchPeople]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parts of Speech\n",
    "\n",
    "Each word already has a part of speech and a tag associated with it. Here's a list of all the parts of speech in _Pride and Prejudice_: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{82: 'ADJ',\n",
       " 83: 'ADP',\n",
       " 84: 'ADV',\n",
       " 86: 'CONJ',\n",
       " 87: 'DET',\n",
       " 88: 'INTJ',\n",
       " 89: 'NOUN',\n",
       " 90: 'NUM',\n",
       " 91: 'PART',\n",
       " 92: 'PRON',\n",
       " 93: 'PROPN',\n",
       " 94: 'PUNCT',\n",
       " 96: 'SYM',\n",
       " 97: 'VERB',\n",
       " 98: 'X',\n",
       " 100: 'SPACE'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a quick-and-dirty lookup table of POS IDs, \n",
    "# since the default representation of a POS is numeric. \n",
    "tagDict = {w.pos: w.pos_ for w in pride} \n",
    "tagDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's fun to compare the distribution of parts of speech in each text: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01df564d30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SmTNnpmPHjo37tt9++8ycOXONxwAAQK0RajZPZkA1j7qGhoaGNX3DyJEjs99+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\n/w/UYAsgnHk5PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01df4df048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grailPOS = pd.Series(grail.count_by(spacy.attrs.POS))/len(grail)\n",
    "pridePOS = pd.Series(pride.count_by(spacy.attrs.POS))/len(pride)\n",
    "\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "df = pd.DataFrame([grailPOS, pridePOS], index=['Grail', 'Pride'])\n",
    "df.columns = [tagDict[column] for column in df.columns]\n",
    "df.T.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see, for instance, what the most common pronouns might be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 2061),\n",
       " ('she', 1370),\n",
       " ('it', 1286),\n",
       " ('you', 1129),\n",
       " ('he', 1088),\n",
       " ('him', 753),\n",
       " ('her', 706),\n",
       " ('they', 474),\n",
       " ('me', 429),\n",
       " ('them', 429)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prideAdjs = [w for w in pride if w.pos_ == 'PRON']\n",
    "Counter([w.string.strip() for w in prideAdjs]).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 254),\n",
       " ('you', 202),\n",
       " ('it', 107),\n",
       " ('we', 62),\n",
       " ('You', 61),\n",
       " ('We', 60),\n",
       " ('me', 46),\n",
       " ('He', 46),\n",
       " ('he', 43),\n",
       " ('It', 35)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grailAdjs = [w for w in grail if w.pos_ == 'PRON']\n",
    "Counter([w.string.strip() for w in grailAdjs]).most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try this on the level of a sentence. First, let's get all the sentences in which Sir Robin is explicitly mentioned: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[The wise Sir Bedevere was the first to join King Arthur's knights, but other illustrious names were soon to follow: Sir Lancelot the Brave; Sir Gallahad the Pure; and Sir Robin the-not-quite-so-brave-as-Sir-Lancelot, who had nearly fought the Dragon of Angnor, who had nearly stood up to the vicious Chicken of Bristol, and who had personally wet himself at the Battle of Badon Hill; and the aptly named Sir Not-appearing-in-this-film.  ,\n",
       " The Tale of Sir Robin.  ,\n",
       " Sir Robin rode north, through the dark forest of Ewing, accompanied by his favorite minstrels.,\n",
       " MINSTREL: [singing] Bravely bold Sir Robin, rode forth from Camelot.  ,\n",
       " He was not afraid to die, O brave Sir Robin.  ,\n",
       " Brave, brave, brave, brave Sir Robin!  ,\n",
       " To have his kneecaps split, and his body burned away, And his limbs all hacked and mangled, brave Sir Robin!,\n",
       " He is brave Sir Robin, brave Sir Robin, who--\n",
       " ROBIN: Shut up!  ,\n",
       " He's scarper\n",
       " MINSTREL: [singing] Brave Sir Robin ran away.,\n",
       " Yes, brave Sir Robin turned about\n",
       " ROBIN: I didn't!,\n",
       " MINSTREL: [singing] Bravest of the brave, Sir Robin.,\n",
       " My liege, it's Sir Robin!,\n",
       " ARTHUR: Sir Robin!,\n",
       " And so, Arthur and Bedevere and Sir Robin set out on their search to find the enchanter of whom the old man had spoken in scene twenty-four.  ,\n",
       " ARTHUR: Sir Robin!,\n",
       " ARTHUR: Brave Sir Robin, you go.,\n",
       " ROBIN: Sir Robin of Camelot.]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "robinSents = [sent for sent in grail.sents if 'Sir Robin' in sent.string]\n",
    "robinSents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's analyze just one of these sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sir Robin rode north, through the dark forest of Ewing, accompanied by his favorite minstrels."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = robinSents[2]\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the tags and parts of speech: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sir NNP PROPN\n",
      "Robin NNP PROPN\n",
      "rode VBD VERB\n",
      "north NN NOUN\n",
      ", , PUNCT\n",
      "through IN ADP\n",
      "the DT DET\n",
      "dark JJ ADJ\n",
      "forest NN NOUN\n",
      "of IN ADP\n",
      "Ewing NNP PROPN\n",
      ", , PUNCT\n",
      "accompanied VBN VERB\n",
      "by IN ADP\n",
      "his PRP$ ADJ\n",
      "favorite JJ ADJ\n",
      "minstrels NNS NOUN\n",
      ". . PUNCT\n",
      "\n",
      " SP SPACE\n"
     ]
    }
   ],
   "source": [
    "for word in r2: \n",
    "    print(word, word.tag_, word.pos_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependency Parsing\n",
    "Now let's analyze the structure of the sentence. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This sentence has lots of properties: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['doc',\n",
       " 'end',\n",
       " 'end_char',\n",
       " 'ent_id',\n",
       " 'ent_id_',\n",
       " 'has_vector',\n",
       " 'label',\n",
       " 'label_',\n",
       " 'lefts',\n",
       " 'lemma_',\n",
       " 'merge',\n",
       " 'noun_chunks',\n",
       " 'orth_',\n",
       " 'rights',\n",
       " 'root',\n",
       " 'sent',\n",
       " 'sentiment',\n",
       " 'similarity',\n",
       " 'start',\n",
       " 'start_char',\n",
       " 'string',\n",
       " 'subtree',\n",
       " 'text',\n",
       " 'text_with_ws',\n",
       " 'vector',\n",
       " 'vector_norm']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[prop for prop in dir(r2) if not prop.startswith('_')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To drill down into the sentence, we can start with the root: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rode"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2.root"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That root has children: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Robin, north, ,, through, accompanied, .]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(r2.root.children)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see all of the children for each word:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sir :  []\n",
      "Robin :  [Sir]\n",
      "rode :  [Robin, north, ,, through, accompanied, .]\n",
      "north :  []\n",
      ", :  []\n",
      "through :  [forest]\n",
      "the :  []\n",
      "dark :  []\n",
      "forest :  [the, dark, of, ,]\n",
      "of :  [Ewing]\n",
      "Ewing :  []\n",
      ", :  []\n",
      "accompanied :  [by]\n",
      "by :  [minstrels]\n",
      "his :  []\n",
      "favorite :  []\n",
      "minstrels :  [his, favorite]\n",
      ". :  [\n",
      "]\n",
      "\n",
      " :  []\n"
     ]
    }
   ],
   "source": [
    "for word in r2: \n",
    "    print(word, ': ', str(list(word.children)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This is very messy-looking, so let's create a nicer visualization. Here I'll be using a class I wrote called sentenceTree, available in the `sent2tree` module in this repository. It just shoehorns a SpaCy span (sentence or other grammatical fragment) into a tree that can be read by the `ete3` library for handling trees. This library just allows for some pretty visualizations of trees. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sir Robin rode north, through the dark forest of Ewing, accompanied by his favorite minstrels.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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BpxCwMB7NfGORla05RnXhwoVqga6Kp6cn2rRpAwA4efKkWlpycjL27dsHABg+fLhaoKui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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = sentenceTree(r2)\n",
    "t, ts = st.render()\n",
    "t.render('%%inline', tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can already see how useful this might be. Since adjectives are typically children of the things they describe, we can get approximations for adjectives that describe characters. How is Sir Robin described? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bold\n",
      "brave\n",
      "brave\n",
      "brave\n",
      "brave\n",
      "brave\n",
      "brave\n",
      "brave\n"
     ]
    }
   ],
   "source": [
    "for sent in robinSents: \n",
    "    for word in sent: \n",
    "        if 'Robin' in word.string: \n",
    "            for child in word.children: \n",
    "                if child.pos_ == 'ADJ':\n",
    "                    print(child)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we shouldn't always trust syntactic insight! Now let's do something similar for Pride and Prejudice. First, we'll use named entity extraction to get a list of the most frequently mentioned characters:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Elizabeth', 587),\n",
       " ('Darcy', 310),\n",
       " ('Bennet', 264),\n",
       " ('Jane', 259),\n",
       " ('Bingley', 200),\n",
       " ('Collins', 179),\n",
       " ('Wickham', 166),\n",
       " ('Lydia', 128),\n",
       " ('Lizzy', 92),\n",
       " ('Gardiner', 87)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter([w.string.strip() for w in pride.ents if w.label_ == 'PERSON']).most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can write a function that walks down the tree from each character, looking for the first adjectives it can find: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def adjectivesDescribingCharacters(text, character):\n",
    "    sents = [sent for sent in text.sents if character in sent.string]\n",
    "    adjectives = []\n",
    "    for sent in sents: \n",
    "        for word in sent: \n",
    "            if character in word.string:\n",
    "                for child in word.children: \n",
    "                    if child.pos_ == 'ADJ': \n",
    "                        adjectives.append(child.string.strip())\n",
    "    return Counter(adjectives).most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try it on Mr. Darcy: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('late', 6),\n",
       " ('present', 2),\n",
       " ('disappointing', 1),\n",
       " ('abominable', 1),\n",
       " ('poor', 1),\n",
       " ('handsome', 1),\n",
       " ('little', 1),\n",
       " ('disagreeable', 1)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectivesDescribingCharacters(pride, 'Darcy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do the same sort of thing, but look for associated verbs. First, let's get all the sentences in which Elizabeth is mentioned:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "elizabethSentences = [sent for sent in pride.sents if 'Elizabeth' in sent.string]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can peek at one of them: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr. Darcy walked off; and Elizabeth\n",
       "remained with no very cordial feelings toward him."
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elizabethSentences[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mr. Darcy walked off; and Elizabeth\n",
      "remained with no very cordial feelings toward him.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Aq4np06dDCFEtXqqbG+Bg9whyuRXquckxdlB9JCYmSm1t1aqV0WNp3bo1AGitekJERERE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\nF7vuNV07uroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = sentenceTree(elizabethSentences[3])\n",
    "t, ts = st.render()\n",
    "t.render('%%inline', tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want the verb associated with Elizabeth, _remained_, not the root verb of the sentence, _walked_, which is associated with Mr. Darcy. So let's write a function that will walk up the dependency tree from a character's name until we get to the first verb. We'll use lemmas instead of the conjugated forms to collapse _remain_, _remains_, and _remained_ into one verb: _remain_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def verbsForCharacters(text, character):\n",
    "    sents = [sent for sent in pride.sents if character in sent.string]\n",
    "    charWords = []\n",
    "    for sent in sents: \n",
    "        for word in sent: \n",
    "            if character in word.string: \n",
    "                charWords.append(word)\n",
    "    charAdjectives = []\n",
    "    for word in charWords: \n",
    "        # Start walking up the list of ancestors \n",
    "        # Until we get to the first verb. \n",
    "        for ancestor in word.ancestors: \n",
    "            if ancestor.pos_.startswith('V'): \n",
    "                charAdjectives.append(ancestor.lemma_.strip())\n",
    "    return Counter(charAdjectives).most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('be', 186),\n",
       " ('say', 66),\n",
       " ('have', 32),\n",
       " ('see', 24),\n",
       " ('cry', 24),\n",
       " ('reply', 22),\n",
       " ('feel', 20),\n",
       " ('make', 19),\n",
       " ('look', 18),\n",
       " ('receive', 10),\n",
       " ('think', 10),\n",
       " ('take', 10),\n",
       " ('find', 10),\n",
       " ('give', 10),\n",
       " ('begin', 9),\n",
       " ('walk', 8),\n",
       " ('listen', 8),\n",
       " ('turn', 8),\n",
       " ('help', 8),\n",
       " ('pass', 7)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elizabethVerbs = verbsForCharacters(pride, 'Elizabeth')\n",
    "elizabethVerbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "darcyVerbs = verbsForCharacters(pride, 'Darcy')\n",
    "janeVerbs = verbsForCharacters(pride, 'Jane')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We can now merge these counts into a single table, and then we can visualize it with Pandas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01dc0280b8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z3QzD3O4yzPj4+CxoCW7n22+/1c2bN3NMTk5z9erVDNt+/PHHLGiJc7lx40aG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\n9+Joaae4L1WqlHbt2iWTySQ/Pz+7TCl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d40479b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def verbsToMatrix(verbCounts): \n",
    "    \"\"\" \n",
    "    Takes verb counts given by verbsForCharacters \n",
    "    and makes Pandas Series out of them, suitabe for combination in \n",
    "    a DataFrame. \n",
    "    \"\"\"\n",
    "    return pd.Series({t[0]: t[1] for t in verbCounts})\n",
    "\n",
    "verbsDF = pd.DataFrame({'Elizabeth': verbsToMatrix(elizabethVerbs), \n",
    "                        'Darcy': verbsToMatrix(darcyVerbs), \n",
    "                        'Jane': verbsToMatrix(janeVerbs)}).fillna(0)\n",
    "verbsDF.plot(kind='bar', figsize=(14,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probabilities\n",
    "\n",
    "SpaCy has a list of probabilities for English words, and these probabilities are automatically associated with each word once we parse the document. Let's see what the distribution is like: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01d403c8d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZvHhxbr/99px44onZuHFjHn/88bS1tWXt2rW57bbbsnbt2vT29uauu+5KXV1d1q9fn4su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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01d4046c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities = [word.prob for word in grail] \n",
    "pd.Series(probabilities).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's peek at some of the improbable words for _Monty Python and the Holy Grail_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clllank',\n",
       " 'pikang',\n",
       " 'eisrequiem',\n",
       " 'bedwere',\n",
       " 'bors',\n",
       " 'erbert',\n",
       " 'pweeng',\n",
       " 'defeator',\n",
       " 'aauuuves',\n",
       " 'angnor',\n",
       " 'mooooooo',\n",
       " 'nnnnniggets',\n",
       " 'ungallant',\n",
       " 'nador',\n",
       " 'chickennn',\n",
       " 'fwump',\n",
       " 'aauuugh',\n",
       " 'zoosh',\n",
       " 'donaeis',\n",
       " 'zoot']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set([word.string.strip().lower() for word in grail if word.prob < -19]))[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do some rudimentary information extraction by counting the improbable words: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bedevere', 61),\n",
       " ('bridgekeeper', 18),\n",
       " ('zoot', 14),\n",
       " ('minstrel', 13),\n",
       " ('concorde', 12),\n",
       " ('rewr', 10),\n",
       " ('crone', 7),\n",
       " ('aaaaugh', 7),\n",
       " ('twong', 4),\n",
       " ('aaaugh', 4),\n",
       " ('ninepence', 3),\n",
       " ('hiyya', 3),\n",
       " ('uuggggggh', 3),\n",
       " ('auuuuuuuugh', 3),\n",
       " ('mercea', 2),\n",
       " ('hiyaah', 2),\n",
       " ('thppt', 2),\n",
       " ('crapper', 2),\n",
       " ('enchanter', 2),\n",
       " ('neee', 2)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter([word.string.strip().lower() \n",
    "         for word in grail \n",
    "         if word.prob < -19.5]).most_common(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are those words for _Pride and Prejudice_? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('rosings', 18),\n",
       " ('parsonage', 13),\n",
       " ('gentlemanlike', 8),\n",
       " ('twelvemonth', 7),\n",
       " ('longbourn', 4),\n",
       " ('quadrille', 4),\n",
       " ('discomposure', 3),\n",
       " ('regimentals', 3),\n",
       " ('suitableness', 3),\n",
       " ('overspread', 3),\n",
       " ('composedly', 3),\n",
       " ('repine', 3),\n",
       " ('fretfully', 2),\n",
       " ('archly', 2),\n",
       " ('unaffectedly', 2),\n",
       " ('panegyric', 2),\n",
       " ('precipitance', 2),\n",
       " ('prepossession', 2),\n",
       " ('discomposed', 2),\n",
       " ('gratulation', 2)]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter([word.string.strip().lower() \n",
    "         for word in pride \n",
    "         if word.prob < -19.5 \n",
    "         and word.is_alpha\n",
    "         and word.pos_ != 'PROPN'] # This time, let's ignore proper nouns.\n",
    "       ).most_common(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do this with ngrams, too, with some fancy Python magic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ngrams(doc, n): \n",
    "    doc = [word for word in doc \n",
    "           if word.is_alpha # Get rid of punctuation\n",
    "           if not word.string.isupper()] # Get rid of all-caps speaker headings\n",
    "    return list(zip(*[doc[i:] for i in range(n)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grailGrams = set(ngrams(grail, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for gram in grailGrams: \n",
    "#     if sum([word.prob for word in gram]) < -40: \n",
    "#         print(gram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for gram in set(ngrams(pride, 3)): \n",
    "#     if sum([word.prob for word in gram]) < -40: \n",
    "#         print(gram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word Embeddings (Word Vectors)\n",
    "\n",
    "Word embeddings (word vectors) are numeric representations of words, usually generated via dimensionality reduction on a word cooccurrence matrix for a large corpus. The vectors SpaCy uses are the [GloVe](http://nlp.stanford.edu/projects/glove/) vectors, Stanford's Global Vectors for Word Representation. These vectors can be used to calculate semantic similarity between words and documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coconut, africanSwallow, europeanSwallow, horse = nlp('coconut'), nlp('African Swallow'), nlp('European Swallow'), nlp('horse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15660886865377041"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coconut.similarity(horse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33525863407350248"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "africanSwallow.similarity(horse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85963767802014357"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "africanSwallow.similarity(europeanSwallow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at vectors for _Pride and Prejudice_. First, let's get the first 150 nouns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prideNouns = [word for word in pride if word.pos_.startswith('N')][:150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get vectors and labels for each of them: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prideNounVecs = [word.vector for word in prideNouns]\n",
    "prideNounLabels = [word.string.strip() for word in prideNouns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prideNounVecs[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "A single vector is 300-dimensional, so in order to plot it in 2D, it might help to reduce the dimensionality to the most meaningful dimensions. We can use Scikit-Learn to perform truncated singular value decomposition for latent semantic analysis (LSA). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lsa = TruncatedSVD(n_components=2)\n",
    "lsaOut = lsa.fit_transform(prideNounVecs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results in a scatter plot: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XKru0Min9y1ZERERERKTcFBYWMm7caPbt24vT6aB9+04MGvQ0AD/++AOjRw/H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zOu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\nUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01e0565e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = lsaOut[:,0], lsaOut[:,1]\n",
    "for i in range(len(xs)): \n",
    "    plt.scatter(xs[i], ys[i])\n",
    "    plt.annotate(prideNounLabels[i], (xs[i], ys[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Document Vectorization\n",
    "\n",
    "This uses a non-semantic technique for vectorizing documents, just using bag-of-words. We won't need any of the fancy features of SpaCy for this, just scikit-learn. We'll use a subset of the Inaugural Address Corpus that contains 20th and 21st century inaugural addresses. \n",
    "\n",
    "First, we'll vectorize the corpus using scikit-learn's `TfidfVectorizer` class. This creates a matrix of word frequencies. (It doesn't actually use TF-IDF, since we're turning that off in the options below.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(input='filename', decode_error='ignore', use_idf=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inauguralFilenames = sorted(glob('inaugural/*'))\n",
    "\n",
    "# Make labels by removing the directory name and .txt extension: \n",
    "labels = [filename.split('/')[1] for filename in inauguralFilenames]\n",
    "labels = [filename.split('.')[0] for filename in labels]\n",
    "\n",
    "# While we're at it, let's make a list of the lengths, so we can use them to plot dot sizes. \n",
    "lengths = [len(open(filename, errors='ignore').read())/100 for filename in inauguralFilenames]\n",
    "\n",
    "# Add a manually compiled list of presidential party affiliations, \n",
    "# So that we can use this to color our dots. \n",
    "parties = 'rrrbbrrrbbbbbrrbbrrbrrrbbrrbr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tfidfOut = tfidf.fit_transform(inauguralFilenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 6177)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidfOut.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lsaOut = lsa.fit_transform(tfidfOut.todense())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NmkSXLl1o2LDhPQlSRERERERE5EHJVo9zxYoV8fPzo1WrVphMJiZMmMDKlSvx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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c4cfd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = lsaOut[:,0], lsaOut[:,1]\n",
    "for i in range(len(xs)): \n",
    "    plt.scatter(xs[i], ys[i], c=parties[i], s=lengths[i], alpha=0.5)\n",
    "    plt.annotate(labels[i], (xs[i], ys[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average Sentence Lengths\n",
    "\n",
    "Let's load the Inaugural Address documents into SpaCy to analyze things like average sentence length. SpaCy makes this really easy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inaugural = [nlp(open(doc, errors='ignore').read()) for doc in inauguralFilenames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentLengths = [ np.mean([len(sent) for sent in doc.sents]) for doc in inaugural ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c4be0198>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t2tRYriRt2LBB8+bN0549e9SmTRvPi9zX19e2F3m/fv10yy23qHLlyp7HXC6X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\n5HA4HA6Hw+FwOBwOMf4fDEwXco0ML1wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c57de400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(sentLengths, index=labels).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Term Frequency Distributions\n",
    "\n",
    "This sort of thing you've probably already seen in the NLTK book, but it's made even easier in SpaCy. We're simply going to count the occurrences of words and divide by the total number of words in the document. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a Pandas Data Frame with each word counted in each document, \n",
    "# divided by the length of the document. \n",
    "inauguralSeries = [pd.Series(Counter(   \n",
    "                    [word.string.strip().lower() \n",
    "                     for word in doc]))/len(doc) \n",
    "                     for doc in inaugural]\n",
    "seriesDict = {label: series for label, series in zip(labels, inauguralSeries)}\n",
    "inauguralDf = pd.DataFrame(seriesDict).T.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>!</th>\n",
       "      <th>\"</th>\n",
       "      <th>$</th>\n",
       "      <th>'</th>\n",
       "      <th>'ll</th>\n",
       "      <th>'s</th>\n",
       "      <th>'ve</th>\n",
       "      <th>(</th>\n",
       "      <th>)</th>\n",
       "      <th>...</th>\n",
       "      <th>yields</th>\n",
       "      <th>you</th>\n",
       "      <th>young</th>\n",
       "      <th>youngest</th>\n",
       "      <th>your</th>\n",
       "      <th>yours</th>\n",
       "      <th>yourselfxand</th>\n",
       "      <th>yourselves</th>\n",
       "      <th>youth</th>\n",
       "      <th>zealously</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1901-McKinley</th>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905-Roosevelt</th>\n",
       "      <td>0.003656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000914</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909-Taft</th>\n",
       "      <td>0.007303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913-Wilson</th>\n",
       "      <td>0.005753</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917-Wilson</th>\n",
       "      <td>0.010161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 6213 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 !         \"         $    '  'll        's  \\\n",
       "1901-McKinley   0.004872  0.000406  0.004872  0.000406  0.0  0.0  0.000406   \n",
       "1905-Roosevelt  0.003656  0.000000  0.000000  0.000000  0.0  0.0  0.000914   \n",
       "1909-Taft       0.007303  0.000000  0.000679  0.000170  0.0  0.0  0.000000   \n",
       "1913-Wilson     0.005753  0.000523  0.001046  0.000000  0.0  0.0  0.002615   \n",
       "1917-Wilson     0.010161  0.000000  0.000000  0.000000  0.0  0.0  0.001195   \n",
       "\n",
       "                've    (    )    ...      yields       you  young  youngest  \\\n",
       "1901-McKinley   0.0  0.0  0.0    ...         0.0  0.000406    0.0       0.0   \n",
       "1905-Roosevelt  0.0  0.0  0.0    ...         0.0  0.000000    0.0       0.0   \n",
       "1909-Taft       0.0  0.0  0.0    ...         0.0  0.000000    0.0       0.0   \n",
       "1913-Wilson     0.0  0.0  0.0    ...         0.0  0.000000    0.0       0.0   \n",
       "1917-Wilson     0.0  0.0  0.0    ...         0.0  0.001195    0.0       0.0   \n",
       "\n",
       "                    your  yours  yourselfxand  yourselves  youth  zealously  \n",
       "1901-McKinley   0.000000    0.0           0.0         0.0    0.0        0.0  \n",
       "1905-Roosevelt  0.000000    0.0           0.0         0.0    0.0        0.0  \n",
       "1909-Taft       0.000000    0.0           0.0         0.0    0.0        0.0  \n",
       "1913-Wilson     0.000000    0.0           0.0         0.0    0.0        0.0  \n",
       "1917-Wilson     0.003586    0.0           0.0         0.0    0.0        0.0  \n",
       "\n",
       "[5 rows x 6213 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inauguralDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily slice this data frame with words we're interested in, and plot those words across the corpus. For example, let's look at the proportions of the words \"America\" and \"world\": "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c4564438>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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as2dPRUVFqW/fvpo4caLrjdKuXTtLRh8HDRpU5Y95pUaPHq2NGzcq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\nqXPwYpXD4XA4HA6Hw+FwOHWO/weiG38Offe87wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c5126780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inauguralDf[['world', 'america']].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even compute, say the ratio of uses of the word \"America\" to uses of the word \"world.\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c5196eb8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WXFyckez+/ftbWVlZ1saNG63JkydbGRkZRn7eTmbHxsZaiYmJVlJS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\nRHzkx+XvDB0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c51c9b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "americaWorldRatio = inauguralDf['america']/inauguralDf['world']\n",
    "americaWorldRatio.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Document Similarity Matrix\n",
    "\n",
    "Using the `.similarity()` method from earlier that uses word vectors, we can very easily compute the document similarity between all the documents in our corpus. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "similarities = [ [doc.similarity(other) for other in inaugural] for doc in inaugural ]\n",
    "similaritiesDf = pd.DataFrame(similarities, columns=labels, index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f01c51feac8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUlIAmD59Ol26dKmw/ejRo0lKSiIpKYmhQ4cyePDgCq83bNgQHx8fjhw5AsC2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01c51d7438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Requires the Seaborn library. \n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "seaborn.heatmap(similaritiesDf)"
   ]
  },
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   "cell_type": "markdown",
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    "# Exercises\n",
    "\n",
    "1. Extract all the events from _Pride and Prejudice_. \n",
    "2. Make a lexical dispersion plot of the word \"ni\" in _Monty Python and the Holy Grail_. What does this tell us? \n",
    "3. Find the shortest sentence in any inaugural address from our corpus.\n",
    "4. Find the president that used the lowest proportions of adjectives (or nouns, verbs) in his inaugural address. \n",
    "5. Find which of Charles Dickens's novels (or those of any other author) are the most semantically similar. \n",
    "\n",
    "# Learn More\n",
    "\n",
    " - [SpaCy Homepage](https://spacy.io/)\n",
    " - [Pycon: NLP in 10 Lines of Code](https://github.com/cytora/pycon-nlp-in-10-lines)\n",
    " - [What You Can Learn About Food By Analyzing a Million Yelp Reviews](http://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb)\n",
    " - [Other Tutorials Listed on Spacy.io](https://spacy.io/docs/usage/tutorials)\n",
    " \n",
    "# See Also\n",
    "\n",
    " - [Textacy, higher-level NLP based on SpaCy](https://github.com/chartbeat-labs/textacy)"
   ]
  }
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